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Copy Number Variation and Transcription Regulation of Genes and Immune Microenvironment of Tumor Affect the Stemness of Prostate Cancer Cell

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Copy Number Variation and Transcription Regulation of Genes and Immune Microenvironment of Tumor Affect the Stemness of Prostate Cancer Cell Copy Number Variation and Transcription Regulation of Genes and Immune Microenvironment of Tumor Affect the Stemness of Prostate Cancer Cell Zao Dai College of Life Sciences, Nanjing Normal University, Nanjing, Jiangsu, China Ping Liu ( [email protected] ) College of Life Sciences, Nanjing Normal University, Nanjing, Jiangsu, China https://orcid.org/0000- 0001-5366-4618 Research Keywords: stemness of prostate cancer, WGCNA, ATAC-seq, CNV,immune inltration Posted Date: November 4th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-100256/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/22 Abstract Background: Prostate cancer stem cells (pCSC) play an important role in tumor metastasis through multiple pathways. From gene, protein to microenvironment, there are many factors affecting the cell stemness of prostate cancer (PCa). However, the effective factors affecting the stemness of prostate cancer cells are still unclear. Methods: Based on the transcription data of prostate cancer in the TCGA database, WGCNA (Weighted Gene Co-expression Network Analysis) and stemness scores were used to nd important stemness gene module. According to ATAC-seq and genomic data, analyze their relationship with stemness. The interaction of stemness genes was analyzed with STRING (functional protein association networks) database. Furthermore, based on the immune microenvironment score, the relationship between immune and stemness was analyzed. Results: The most important stemness gene module in prostate cancer was obtained with WGCNA method; then a positive correlation between the gene CNVs (Copy Number Variants) of the most important stemness gene module and PCa stemness was found, as well as a positive correlation between the gene CNVs and Gleason score of PCa was also drug out. Further, the key transcriptional regulators of the most important stemness genes in PCa were obtained. In addition, it's found that immune cells, especially CD8+T cells and M1 macrophages, suppressed the stemness of PCa cells. Finally, by analyzing the protein interactions and the relationship between genes and immune cells, we found that interaction of the proteins of the most important stemness genes module and the relationship between these genes and immune cells of microenvironment of PCa were all important in affecting the stemness of PCa cells. Conclusions: By analyzing multi-omics data of clinical specimen, we got the most important stemness genes and their important transcriptional regulators in PCa; and further mining analysis showed that the stemness of PCa cells is positive regulated by the CNVs and the interaction of the proteins of the most important stemness genes, and negatively regulated by the immune cells of the microenvironment of prostate cancer. Background Cancer stem cells (CSCs) are a few stemness-like cells with the ability of self-renewal and differentiation into cancer cells [1]. They play an important role in the occurrence and development of tumors, especially closely related to tumor metastasis [2-4]. In prostate cancer, the stemness of cancer cells (including prostate cancer stem cells, PCSC) are closely related to the metastasis of prostate cancer [5]. In the process of prostate cancer metastasis, PCSCs initiates EMT to form broblast-like cells, and then enter the blood. With the circulation system, prostate cancer cells migrate to other tissues (such as bone tissue and lymph tissue), and grow into tumor tissue in other tissues, which leads to tumor metastasis (cancer cell spreading). Page 2/22 As we known, many factors relate to stemness of CSC cells, not only including intracellular factors (such as stemness-related genes), but also including microenvironment of cancer tissues (such as immune cells in tumor microenvironment) [3, 6, 7]. In prostate cancer, it has been reported that the immune cells (especially CD8+ T cells and macrophages) in the microenvironment of prostate cancer are closely related to the metastasis of PCa cells [8, 9]. The number of immune cells around the early PCa tissue will decrease with the growth of the cancer tissue, which results in the immunity in the microenvironment of PCa also decrease [10]. With the development of PCa to the later stage (Gleason score to 6-10), some studies report that some immune cells (such as related T cells) in the microenvironment can reverse to promote or enhance the growth of PCa cells and help cancer cells metastasize [11]. Although increasing publications have reported the relationship between stemness and metastasis in PCa cells, there are few of studies on the stemness regulation of PCa cells [12, 13]. Lots of factors affecting the stemness of PCa cells remain unclear and need to investigate. The bioinformatics method based on the TCGA database has been increasingly used to analyze the molecular basis of prostate cancer development and clinical patient prognosis [14-16]. By using appropriate analytical software and methods to analyze a variety of large size data of clinial specimen from the TCG database (including transcriptome sequencing data, gene sequencing data, ATAC-seq data, etc.), the molecular basis of prostate tumorigenesis, development of PCa and the prognosis of patients might be gure out [17, 18]. Therefore, the bioinformatics analysis of TCGA data should be helpful to provide some clues and direction for basic experimental research and clinical cancer treatment in PCa. In this study, we obtained the most important stemness-related gene module in prostate cancer cells from transcriptome data and OCLR scores (for scoring the stemness of tumors). Further mining analysis showed that the cancer cell stemness in PCa tissues was positively correlated with the CNVs of the most important stemness-related genes and negatively correlated with the number of immune cells in the microenvironment of PCa tissue, and these correlations were all closely related to the clinical stage of PCa (such as Gleason score). Analysis results also demonstrated that some transcriptional regulators of the most important stemness-related genes were important in regulating the stemness of PCa cells. All our multi-omics analyzing results might provide some theoretical clues for us to experimentally investigate the factors affecting the PCa cell stemness and its relationships between PCSC and PCa metastasis. Methods Analysis of transcriptome (RNA-seq) data Based on OCLR stemness scores and Gleason classication of TCGA prostate cancer clinical data, correlation analysis between PCa cell stemness and Gleason scores were carried out. By combing stemness scoreing, WGCNA [19] analysis was performed on the transcriptome data of prostate cancer in TCGA. Differential expression analysis of genes in the WGCNA results that were most relevant to PCa cell stemness was performed and presented in heatmap. Further, the transcriptome data of 33 samples from Page 3/22 GSE104786 (GEO database) were also analyzed for differential expression of stemness-related genes, and heatmap was drawn. In addition, the transcriptome data of SRR7651698, SRR7651699, SRR7651700, SRR7651715, SRR7651716, SRR7651717, SRR7651718, SRR7651719, SRR7651720 (SRA database) were used to analyze the difference of gene expression in prostate cancer lines and small cell carcinoma of prostate. Analysis of gene CNV data The genes most related to stemness in WGCNA results were screened and their locations in genome were obtained by local Perl script method. Combined with the CNV data of TCGA, the local Perl script was used to screen the segments containing the locations of stemness genes; and then by combined with Gleason score of TCGA prostate cancer samples, the CNV of stemness genes were calculated by GISTIC2.0 [20]. Analysis of ATAC-seq data From TCGA database, we got the ATAC-seq data (including SRR7651660, SRR7651661, SRR7651662, SRR7651675 SRR7651676); and then 2kb data of upstream and downstream of the stemness genes were obtained by using Bowtie2 software [21]. According to the results, the TSS signal intensity of stemness genes was drown by using deeptools [22]. After analyzing the above alignment data using MACS [23] and HOMER [24], information about transcription regulators of the stemness genes were obtained; and the importance of each transcriptional factors were further obtained from TCGA transcriptome data by PCA analysis with R language. As an example, visualized the upstream transcriptional factor of EZH2 with Sushi [25] package in R language. Analysis of tumor microenvironment and immune inltration The tumor microenvironment score and tumor immune inltration score were calculated using ESTIMATE [26] and CIBERSORT [27], respectively. Then, the correlation between stemness score and immune score of PCa cells was calculated with R by combining with Gleason classication of prostate cancer. Based on CIBERSORT analysis results, the distribution map of immune cell components of different Gleason grades in prostate cancer were drawn with R, and the correlation network among immune cells was also drawn with R by according to the correlation and signicance of different immune cells. Protein interaction network and correlation between stemness genes and immune inltration After getting and analyzing PPI (Protein-Protein Interaction) data from the STRING (functional protein association networks) database, the PPI data of important stemness genes was obtained. And then interaction network of proteins of important stemness genes was drawn with Cytoscape [28]. In addition,
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